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Developing and reusing bioinformatics data analysis pipelines using scientific workflow systems.

Marine Djaffardjy1, George Marchment1, Clémence Sebe1

  • 1Universite Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay 91405, France.

Computational and Structural Biotechnology Journal
|March 27, 2023
PubMed
Summary
This summary is machine-generated.

Scripting languages are insufficient for large-scale bioinformatics data analysis. Scientific workflow systems offer modular, reproducible, and reusable pipelines for complex data challenges.

Keywords:
BioinformaticsReproducibilityReuseScientific workflows

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Scripting languages like Python and R are common for small-scale bioinformatics data analysis.
  • These tools are insufficient for large-scale, shareable, maintainable, and reusable pipelines.
  • Handling large datasets and high-performance computing clusters requires more robust solutions.

Purpose of the Study:

  • To outline key requirements for building large-scale bioinformatics data pipelines.
  • To map existing solutions that meet these requirements.
  • To highlight the benefits of scientific workflow systems for modular, reproducible, and reusable pipelines.

Main Methods:

  • Literature review of existing solutions for large-scale data pipelines.
  • Empirical study of a large collection of workflows to analyze reuse practices.

Main Results:

  • Identified key requirements for developing robust bioinformatics data pipelines.
  • Evaluated various solutions, highlighting scientific workflow systems.
  • Analyzed current practices in workflow reuse within the bioinformatics community.

Conclusions:

  • Scientific workflow systems are crucial for developing modular, reproducible, and reusable bioinformatics pipelines.
  • Transitioning from scripting languages to workflow systems is essential for large-scale data analysis.
  • Understanding workflow reuse practices can improve pipeline development and sharing.